Remote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models

Author(s)

Berveglieri, Adilson ; Imai, Nilton Nobuhiro ; Watanabe, Fernanda Sayuri Yoshino ; Tommaselli, Antonio Maria Garcia ; Ederli, Gloria Maria Padovani ; de Araujo, Fabio Fernandes ; Lupatini, Gelci Carlos ; Honkavaara, Eija

• Document type: Journal article
• Source: AGRIENGINEERING; v. 6, n. 3, p. 19-pg., 2024-09-01.

Abstract

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson's correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction. (AU)

• FAPESP's process: 21/10823-0 - Microbial community and its relationship with the suppression of nematodes in a crop-livestock integration system
• Grantee: Fabio Fernando de Araujo
• Support Opportunities: Regular Research Grants
• FAPESP's process: 21/06029-7 - High resolution remote sensing for digital agriculture
• Grantee: Antonio Maria Garcia Tommaselli
• Support Opportunities: Research Projects - Thematic Grants